Overview

Dataset statistics

Number of variables11
Number of observations865
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.5 KiB
Average record size in memory88.1 B

Variable types

DateTime1
Numeric10

Alerts

Open is highly correlated with High and 4 other fieldsHigh correlation
High is highly correlated with Open and 4 other fieldsHigh correlation
Low is highly correlated with Open and 4 other fieldsHigh correlation
Close is highly correlated with Open and 4 other fieldsHigh correlation
Adj Close is highly correlated with Open and 4 other fieldsHigh correlation
S_10 is highly correlated with Open and 4 other fieldsHigh correlation
Open-Close is highly correlated with Volume and 1 other fieldsHigh correlation
Open-Open is highly correlated with Volume and 1 other fieldsHigh correlation
Volume is highly correlated with Open-Close and 1 other fieldsHigh correlation
Date has unique values Unique
Corr has unique values Unique
Open-Close has 19 (2.2%) zeros Zeros

Reproduction

Analysis started2022-11-10 10:23:58.534832
Analysis finished2022-11-10 10:24:08.272227
Duration9.74 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Minimum2019-06-06 00:00:00
Maximum2022-11-08 00:00:00
2022-11-10T15:54:08.332341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:08.456033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Open
Real number (ℝ≥0)

HIGH CORRELATION

Distinct745
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.82716765
Minimum15.96000004
Maximum63.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:08.568404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15.96000004
5-th percentile23.34199982
Q130.51000023
median35.47000122
Q344.99000168
95-th percentile56.31199951
Maximum63.25
Range47.28999996
Interquartile range (IQR)14.48000145

Descriptive statistics

Standard deviation9.908622816
Coefficient of variation (CV)0.2619446137
Kurtosis-0.6420970844
Mean37.82716765
Median Absolute Deviation (MAD)6.909999847
Skewness0.4539407918
Sum32720.50001
Variance98.18080611
MonotonicityNot monotonic
2022-11-10T15:54:08.666878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335
 
0.6%
454
 
0.5%
314
 
0.5%
373
 
0.3%
30.399999623
 
0.3%
29.110000613
 
0.3%
413
 
0.3%
36.53
 
0.3%
32.099998473
 
0.3%
32.330001833
 
0.3%
Other values (735)831
96.1%
ValueCountFrequency (%)
15.960000041
0.1%
17.760000231
0.1%
20.149999621
0.1%
20.180000311
0.1%
20.370000841
0.1%
20.700000761
0.1%
20.930000311
0.1%
211
0.1%
21.040000921
0.1%
21.069999691
0.1%
ValueCountFrequency (%)
63.251
 
0.1%
621
 
0.1%
61.020000461
 
0.1%
60.740001681
 
0.1%
60.349998471
 
0.1%
60.330001831
 
0.1%
60.119998931
 
0.1%
60.110000611
 
0.1%
603
0.3%
59.990001681
 
0.1%

High
Real number (ℝ≥0)

HIGH CORRELATION

Distinct782
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.59789251
Minimum17.79999924
Maximum64.05000305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:08.765878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum17.79999924
5-th percentile23.93199959
Q131.25
median36.31000137
Q345.63999939
95-th percentile57.60200043
Maximum64.05000305
Range46.25000381
Interquartile range (IQR)14.38999939

Descriptive statistics

Standard deviation9.975656343
Coefficient of variation (CV)0.2584508038
Kurtosis-0.6472829676
Mean38.59789251
Median Absolute Deviation (MAD)6.98000145
Skewness0.4670898675
Sum33387.17702
Variance99.51371948
MonotonicityNot monotonic
2022-11-10T15:54:08.865716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.349998474
 
0.5%
44.799999244
 
0.5%
343
 
0.3%
44.240001683
 
0.3%
30.149999623
 
0.3%
49.950000763
 
0.3%
46.659999853
 
0.3%
47.689998633
 
0.3%
47.939998633
 
0.3%
35.099998473
 
0.3%
Other values (772)833
96.3%
ValueCountFrequency (%)
17.799999241
0.1%
20.309999471
0.1%
21.125999451
0.1%
21.260000231
0.1%
21.370000841
0.1%
21.399999621
0.1%
21.465000151
0.1%
21.489999771
0.1%
21.610000611
0.1%
21.780000692
0.2%
ValueCountFrequency (%)
64.050003051
0.1%
63.51
0.1%
61.51
0.1%
61.310001371
0.1%
61.090000151
0.1%
61.060001371
0.1%
61.020000461
0.1%
61.009998321
0.1%
60.950000761
0.1%
60.930000311
0.1%

Low
Real number (ℝ≥0)

HIGH CORRELATION

Distinct775
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.96529825
Minimum13.71000004
Maximum60.79999924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:08.964797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum13.71000004
5-th percentile22.45800056
Q129.82999992
median34.51499939
Q343.95000076
95-th percentile54.97799988
Maximum60.79999924
Range47.0899992
Interquartile range (IQR)14.12000084

Descriptive statistics

Standard deviation9.767729332
Coefficient of variation (CV)0.2642405119
Kurtosis-0.6574173423
Mean36.96529825
Median Absolute Deviation (MAD)6.664999008
Skewness0.4273275148
Sum31974.98299
Variance95.4085363
MonotonicityNot monotonic
2022-11-10T15:54:09.060160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553
 
0.3%
35.259998323
 
0.3%
33.549999243
 
0.3%
32.53
 
0.3%
48.529998783
 
0.3%
29.360000613
 
0.3%
43.560001373
 
0.3%
31.409999853
 
0.3%
313
 
0.3%
30.479999542
 
0.2%
Other values (765)836
96.6%
ValueCountFrequency (%)
13.710000041
0.1%
15.699999811
0.1%
18.010000231
0.1%
19.100000381
0.1%
19.729999541
0.1%
19.895000461
0.1%
20.155000691
0.1%
20.370000841
0.1%
20.430000311
0.1%
20.59099961
0.1%
ValueCountFrequency (%)
60.799999241
0.1%
60.395000461
0.1%
59.840000151
0.1%
59.549999241
0.1%
59.540000921
0.1%
59.314998631
0.1%
59.180000311
0.1%
59.119998931
0.1%
58.950000761
0.1%
58.751
0.1%

Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct756
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.77294798
Minimum14.81999969
Maximum63.18000031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:09.156365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.81999969
5-th percentile23.20800056
Q130.45999908
median35.22999954
Q344.68999863
95-th percentile56.08800049
Maximum63.18000031
Range48.36000061
Interquartile range (IQR)14.22999954

Descriptive statistics

Standard deviation9.850326145
Coefficient of variation (CV)0.2607772671
Kurtosis-0.6364361054
Mean37.77294798
Median Absolute Deviation (MAD)6.840000153
Skewness0.4527817066
Sum32673.6
Variance97.02892517
MonotonicityNot monotonic
2022-11-10T15:54:09.253530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.680000313
 
0.3%
32.680000313
 
0.3%
34.259998323
 
0.3%
33.409999853
 
0.3%
45.720001223
 
0.3%
39.700000763
 
0.3%
33.049999243
 
0.3%
21.53
 
0.3%
31.370000843
 
0.3%
27.819999693
 
0.3%
Other values (746)835
96.5%
ValueCountFrequency (%)
14.819999691
0.1%
18.909999851
0.1%
20.290000921
0.1%
20.459999081
0.1%
20.469999311
0.1%
20.489999771
0.1%
20.649999621
0.1%
21.090000151
0.1%
21.190000531
0.1%
21.329999921
0.1%
ValueCountFrequency (%)
63.180000311
0.1%
60.810001371
0.1%
60.740001681
0.1%
60.709999081
0.1%
60.639999391
0.1%
60.630001071
0.1%
60.520000461
0.1%
60.349998472
0.2%
60.189998631
0.1%
59.610000611
0.1%

Adj Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct756
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.77294798
Minimum14.81999969
Maximum63.18000031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:09.353886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.81999969
5-th percentile23.20800056
Q130.45999908
median35.22999954
Q344.68999863
95-th percentile56.08800049
Maximum63.18000031
Range48.36000061
Interquartile range (IQR)14.22999954

Descriptive statistics

Standard deviation9.850326145
Coefficient of variation (CV)0.2607772671
Kurtosis-0.6364361054
Mean37.77294798
Median Absolute Deviation (MAD)6.840000153
Skewness0.4527817066
Sum32673.6
Variance97.02892517
MonotonicityNot monotonic
2022-11-10T15:54:09.451381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.680000313
 
0.3%
32.680000313
 
0.3%
34.259998323
 
0.3%
33.409999853
 
0.3%
45.720001223
 
0.3%
39.700000763
 
0.3%
33.049999243
 
0.3%
21.53
 
0.3%
31.370000843
 
0.3%
27.819999693
 
0.3%
Other values (746)835
96.5%
ValueCountFrequency (%)
14.819999691
0.1%
18.909999851
0.1%
20.290000921
0.1%
20.459999081
0.1%
20.469999311
0.1%
20.489999771
0.1%
20.649999621
0.1%
21.090000151
0.1%
21.190000531
0.1%
21.329999921
0.1%
ValueCountFrequency (%)
63.180000311
0.1%
60.810001371
0.1%
60.740001681
0.1%
60.709999081
0.1%
60.639999391
0.1%
60.630001071
0.1%
60.520000461
0.1%
60.349998472
0.2%
60.189998631
0.1%
59.610000611
0.1%

Volume
Real number (ℝ≥0)

HIGH CORRELATION

Distinct864
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25573638.73
Minimum3380000
Maximum130965700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:09.549371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3380000
5-th percentile7771600
Q115787900
median21976800
Q330935900
95-th percentile54002480
Maximum130965700
Range127585700
Interquartile range (IQR)15148000

Descriptive statistics

Standard deviation15895280.1
Coefficient of variation (CV)0.6215494115
Kurtosis8.297154503
Mean25573638.73
Median Absolute Deviation (MAD)7439500
Skewness2.245623901
Sum2.21211975 × 1010
Variance2.526599295 × 1014
MonotonicityNot monotonic
2022-11-10T15:54:09.653123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155709002
 
0.2%
164037001
 
0.1%
262328001
 
0.1%
142603001
 
0.1%
142306001
 
0.1%
288688001
 
0.1%
367173001
 
0.1%
303688001
 
0.1%
253740001
 
0.1%
321650001
 
0.1%
Other values (854)854
98.7%
ValueCountFrequency (%)
33800001
0.1%
37757001
0.1%
38153001
0.1%
40210001
0.1%
40787001
0.1%
44120001
0.1%
49740001
0.1%
49866001
0.1%
51101001
0.1%
52516001
0.1%
ValueCountFrequency (%)
1309657001
0.1%
1156018001
0.1%
1139150001
0.1%
1123258001
0.1%
1079263001
0.1%
1066317001
0.1%
1018182001
0.1%
954551001
0.1%
924688001
0.1%
895860001
0.1%

S_10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct862
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.84440925
Minimum21.58099995
Maximum60.0720005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2022-11-10T15:54:09.753531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.58099995
5-th percentile23.37700001
Q130.73900032
median35.66499977
Q344.55400009
95-th percentile55.9496003
Maximum60.0720005
Range38.49100056
Interquartile range (IQR)13.81499977

Descriptive statistics

Standard deviation9.683037721
Coefficient of variation (CV)0.2558644173
Kurtosis-0.7226258281
Mean37.84440925
Median Absolute Deviation (MAD)6.769999886
Skewness0.4504223274
Sum32735.414
Variance93.7612195
MonotonicityNot monotonic
2022-11-10T15:54:09.856213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.908000182
 
0.2%
43.570999912
 
0.2%
44.055000312
 
0.2%
41.699999621
 
0.1%
41.121999741
 
0.1%
40.374999621
 
0.1%
40.301999661
 
0.1%
40.239999771
 
0.1%
40.181999591
 
0.1%
40.11599961
 
0.1%
Other values (852)852
98.5%
ValueCountFrequency (%)
21.580999951
0.1%
21.603000071
0.1%
21.607999991
0.1%
21.662999921
0.1%
21.702000051
0.1%
21.720999911
0.1%
21.780999951
0.1%
21.826000021
0.1%
21.840999981
0.1%
21.849000171
0.1%
ValueCountFrequency (%)
60.07200051
0.1%
60.018000411
0.1%
59.830000311
0.1%
59.745000461
0.1%
59.416000371
0.1%
59.395000081
0.1%
58.869999691
0.1%
58.846000291
0.1%
58.8251
0.1%
58.618999861
0.1%

Corr
Real number (ℝ)

UNIQUE

Distinct865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3492483339
Minimum-0.8971070279
Maximum0.9894741578
Zeros0
Zeros (%)0.0%
Negative223
Negative (%)25.8%
Memory size6.9 KiB
2022-11-10T15:54:09.954912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8971070279
5-th percentile-0.5321411155
Q1-0.01384845299
median0.4672296735
Q30.7730052605
95-th percentile0.9246571484
Maximum0.9894741578
Range1.886581186
Interquartile range (IQR)0.7868537135

Descriptive statistics

Standard deviation0.4806273313
Coefficient of variation (CV)1.376176447
Kurtosis-0.8199726671
Mean0.3492483339
Median Absolute Deviation (MAD)0.349977253
Skewness-0.5878968539
Sum302.0998089
Variance0.2310026316
MonotonicityNot monotonic
2022-11-10T15:54:10.290469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.50389105121
 
0.1%
0.81360909791
 
0.1%
0.26314226821
 
0.1%
0.31307382411
 
0.1%
0.25200048581
 
0.1%
0.63468204421
 
0.1%
0.80117341771
 
0.1%
0.77155864061
 
0.1%
0.614259271
 
0.1%
0.48344466811
 
0.1%
Other values (855)855
98.8%
ValueCountFrequency (%)
-0.89710702791
0.1%
-0.8804821731
0.1%
-0.84790657581
0.1%
-0.79639377941
0.1%
-0.77496489251
0.1%
-0.75878083691
0.1%
-0.75370841481
0.1%
-0.74440706321
0.1%
-0.71141563581
0.1%
-0.70767894931
0.1%
ValueCountFrequency (%)
0.98947415781
0.1%
0.98655885551
0.1%
0.98472708441
0.1%
0.98399179191
0.1%
0.98094130791
0.1%
0.97926162191
0.1%
0.97713165931
0.1%
0.9754173921
0.1%
0.97218174241
0.1%
0.96713833391
0.1%

Open-Close
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct411
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03391908965
Minimum-3.409999847
Maximum4.88999939
Zeros19
Zeros (%)2.2%
Negative397
Negative (%)45.9%
Memory size6.9 KiB
2022-11-10T15:54:10.394615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.409999847
5-th percentile-1.101998901
Q1-0.25
median0.02000045776
Q30.3150005341
95-th percentile1.178000641
Maximum4.88999939
Range8.299999237
Interquartile range (IQR)0.5650005341

Descriptive statistics

Standard deviation0.757412958
Coefficient of variation (CV)22.32999074
Kurtosis5.996444882
Mean0.03391908965
Median Absolute Deviation (MAD)0.2900009155
Skewness0.3484064571
Sum29.34001255
Variance0.573674389
MonotonicityNot monotonic
2022-11-10T15:54:10.487140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
2.2%
0.0200004577614
 
1.6%
-0.029998779311
 
1.3%
0.180000305211
 
1.3%
0.0699996948210
 
1.2%
-0.110000610410
 
1.2%
-0.020000457769
 
1.0%
-0.069999694829
 
1.0%
0.13999938968
 
0.9%
-0.090000152598
 
0.9%
Other values (401)756
87.4%
ValueCountFrequency (%)
-3.4099998471
0.1%
-3.1800003051
0.1%
-2.9799995421
0.1%
-2.7900009161
0.1%
-2.5100021361
0.1%
-2.4700012211
0.1%
-2.4500007631
0.1%
-2.4200019841
0.1%
-2.4200000761
0.1%
-2.3000011441
0.1%
ValueCountFrequency (%)
4.889999391
0.1%
3.889999391
0.1%
3.51
0.1%
3.139999391
0.1%
2.889999391
0.1%
2.7999992371
0.1%
2.5499992371
0.1%
2.4400024411
0.1%
2.4199981691
0.1%
2.389999391
0.1%

Open-Open
Real number (ℝ)

HIGH CORRELATION

Distinct566
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01768786039
Minimum-5.479999542
Maximum6.570000648
Zeros5
Zeros (%)0.6%
Negative448
Negative (%)51.8%
Memory size6.9 KiB
2022-11-10T15:54:10.583272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.479999542
5-th percentile-2.198000336
Q1-0.7350006104
median-0.03999900818
Q30.7200012207
95-th percentile2.077998352
Maximum6.570000648
Range12.05000019
Interquartile range (IQR)1.455001831

Descriptive statistics

Standard deviation1.330979613
Coefficient of variation (CV)-75.24819758
Kurtosis2.383784262
Mean-0.01768786039
Median Absolute Deviation (MAD)0.7199993134
Skewness0.1628849725
Sum-15.29999924
Variance1.771506731
MonotonicityNot monotonic
2022-11-10T15:54:10.684976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.57
 
0.8%
-0.59000015267
 
0.8%
-0.15999984747
 
0.8%
0.38999938966
 
0.7%
-0.756
 
0.7%
-0.70000076296
 
0.7%
-0.049999237065
 
0.6%
-0.38999938965
 
0.6%
05
 
0.6%
0.13999938965
 
0.6%
Other values (556)806
93.2%
ValueCountFrequency (%)
-5.4799995421
0.1%
-4.7000007631
0.1%
-4.6499996191
0.1%
-4.6499977111
0.1%
-4.2599983221
0.1%
-4.0599975591
0.1%
-3.9200000761
0.1%
-3.860000611
0.1%
-3.639999391
0.1%
-3.51
0.1%
ValueCountFrequency (%)
6.5700006481
0.1%
5.9199981691
0.1%
5.389999391
0.1%
5.0599994661
0.1%
4.8700008391
0.1%
4.0400009161
0.1%
3.8200016021
0.1%
3.4599990841
0.1%
3.4500007631
0.1%
3.3400001531
0.1%

Interactions

2022-11-10T15:54:07.028801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:58.794739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.630770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.730074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.578220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.417708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.261928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.147948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.050559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.205288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.115909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:58.876872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.710215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.810524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.659796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.498931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.348513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.236091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.137942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.287529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.202342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:58.957363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.792734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.895825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.741056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.581405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.435596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.323415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.224144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.365957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.285889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.036733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.874988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.975015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.821489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.663712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.518742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.408788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.310547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.445551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.371119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.117985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.217717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.058436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.906658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.744217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.603236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.495209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.653844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.523792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.455408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.198606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.295799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.139990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.987983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.828338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.690490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.583379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.738156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.606246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.543868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.283638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.382592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.227059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.074421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.916547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.784784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.677618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.830344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.692535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.639007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.372982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.472112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.321359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.164577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.008909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.880890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.771959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:05.928548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.781807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.750171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.463037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.564400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.411806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.252980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.100123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.978227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.870132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.023952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.870245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:07.853165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:53:59.544613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:00.644829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:01.491137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:02.332315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:03.178594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.060487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:04.955484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.113181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T15:54:06.945375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-10T15:54:10.772524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-10T15:54:10.883671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-10T15:54:10.996590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-10T15:54:11.114742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-10T15:54:11.233893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-10T15:54:08.015234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-10T15:54:08.202455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateOpenHighLowCloseAdj CloseVolumeS_10CorrOpen-CloseOpen-Open
02019-06-0645.00000045.75000044.27999944.91999844.9199981640370041.7000000.5038910.0000002.130001
12019-06-0744.91999845.66999844.13000144.16000044.1600001265470042.0689990.5675170.000000-0.080002
22019-06-1044.02000044.59000042.52999942.61000142.6100011161870042.1790000.495415-0.139999-0.899998
32019-06-1143.22000143.65000241.79999942.45000142.450001909500042.3290000.4311820.610001-0.799999
42019-06-1242.52000042.65000241.70999942.16999842.169998596530042.5520000.3288510.070000-0.700001
52019-06-1343.04999944.34999842.79999944.31000144.3100011017890043.0030000.3619110.8800010.529999
62019-06-1444.75000044.79999943.11000143.23000043.230000790220043.2850000.1326780.4399991.700001
72019-06-1743.27999944.07899942.93000043.77999943.779999655760043.538000-0.1387910.049999-1.470001
82019-06-1844.29999944.88999943.75000043.86000143.860001731360043.649000-0.2775320.5200001.020000
92019-06-1944.45999945.50000043.95000144.86000144.8600011033150043.6350000.1604960.5999980.160000

Last rows

DateOpenHighLowCloseAdj CloseVolumeS_10CorrOpen-CloseOpen-Open
8552022-10-2628.17000028.98000027.86500028.20000128.2000011731590027.089-0.636264-0.1900010.170000
8562022-10-2728.40000028.76000027.66000027.82000027.8200001676860027.371-0.2933990.1999990.230000
8572022-10-2827.80999927.87999926.49000027.50000027.5000002711670027.650-0.142812-0.010000-0.590000
8582022-10-3127.61000127.73000026.29999926.57000026.5700003732700027.713-0.5209660.110001-0.199999
8592022-11-0130.07000031.00000029.12000129.75000029.7500008799400027.9270.1538113.5000002.459999
8602022-11-0229.57000030.16000028.62999928.80999928.8099993708350028.0550.248417-0.180000-0.500000
8612022-11-0328.03000129.52000027.70999928.73000028.7300002803320028.1410.312576-0.779999-1.539999
8622022-11-0429.44000129.72500028.04000128.38999928.3899992229240028.1780.3405720.7100011.410000
8632022-11-0728.70500028.82500127.61500027.69000127.6900011920410028.1820.1792570.315001-0.735001
8642022-11-0827.57000028.33000026.82000027.44000127.4400012703390028.0900.213816-0.120001-1.135000